Data sourced from Dakota Marketplace, the global LP and GP intelligence platform trusted by thousands of investment professionals. Learn More | Book a Demo
AI tools like Claude and ChatGPT can now connect directly to databases… and for anyone working in private markets, that's a major unlock.
But only if you're actually using it.
The professionals pulling ahead right now aren't waiting for AI to become part of their firm's official process. They're building it into their daily workflow today whether for meeting prep, prospect research, outreach, or competitive intelligence.
The difference between a generic AI tool and the Claude App connected to Dakota Marketplace is the difference between a guess and a grounded answer.
Generic AI has no access to 30 years of verified LP, GP, fund, and transaction data. It hallucinates. It generalizes.
Dakota Marketplace’s Claude App doesn't return rows. It returns intelligence, built on the only dataset built exclusively for the private markets community.
Here's what that looks like in practice, five things we learned today.
1. The Venture Fund III LP Targeting List
For: Fundraisers at seed and Series A-focused venture funds. The Job: Identifying endowments, foundations, and family offices with recent VC commitments that match a Fund III's check size range.
The prompt I'm raising a $250M Fund III focused on seed and Series A B2B SaaS. Using Dakota Marketplace, identify endowments, foundations, and family offices under $2B AUM that have made a venture capital commitment in the last 24 months, with typical check sizes between $2M and $10M. For each, show AUM, venture allocation percentage, most recent VC commitment (manager, vintage, amount), and the best contact with title and email. Rank by fit to our check size and stage focus.
2. The Asset-Based Finance Insurance GA List
For: Managing directors at specialty finance and asset-based finance managers. The Job: Building an insurance general account target list for a structured credit or ABF strategy, including sidecar and rated-note candidates.
The prompt I'm raising for an asset-based finance strategy targeting insurance general accounts. Using Dakota Marketplace, pull U.S. and Bermuda-domiciled insurers with $3B+ in invested assets that have a documented structured credit or ABF allocation, or that use a sidecar/rated-note structure. Show current fixed income allocation, CIO or Head of Investments contact, and any recent manager additions in the space.
These prompts are only as good as the data behind them. Every prompt above runs on Dakota Marketplace data: the verified contacts, AUM, investment preferences, and transaction activity that turn a generic AI answer into a real prospect list. Whichever AI app you use, the facts come from the same place. Book a demo of Dakota Marketplace to get connected.
3. The European Real Estate Debt Roadshow List
For: Heads of institutional distribution at European real estate debt funds. The Job: Mapping UK, Dutch, and German pensions and insurers with real estate debt exposure ahead of a cross-border raise.
The prompt I'm raising a €1B European real estate debt fund. Using Dakota Marketplace and web research, identify UK, Dutch, and German pension funds and insurers with $5B+ AUM and a documented real estate debt or private credit allocation. Show AUM, allocation target, key contact, and whether they've worked with a non-domestic manager before.
4. The OCIO Model Portfolio Inclusion List
For: Heads of consultant relations at open-end infrastructure funds. The Job: Identifying OCIOs with infrastructure model portfolios and recent approved-list activity.
The prompt I want our open-end core infrastructure fund included in more OCIO model portfolios. Using Dakota Marketplace, identify OCIOs managing $10B+ in outsourced assets with infrastructure model portfolio allocations, show the OCIO's infrastructure research lead, their approved manager list size, and any recent additions or removals from that list.
5. The Distressed Debt Cycle-Timing List
For: Managing directors at distressed debt and special situations funds. The Job: Flagging institutions signaling a credit-cycle turn before it shows up in a formal search.
The prompt
I run capital formation for a $2B distressed debt and special situations fund. Using Dakota Marketplace, identify pensions, endowments, and insurance companies with AUM above $3B that increased their opportunistic credit or distressed allocation in the last 12 months, or that have publicly discussed preparing for a credit cycle turn. For each, show current allocation, most recent related commitment, and the key decision-maker contact. Flag the five most time-sensitive targets.
Start Prompting With Real Data
Here's the thing that makes these prompts work… on its own, AI is brilliant at structure and terrible at facts it doesn't have. Ask any chatbot for a pension fund's current allocation, a CIO's contact, or who actually owns a target company, and it will confidently make something up.
That's the whole reason these prompts run on Dakota Marketplace data, no matter which AI app you prefer: you get the speed and structure of AI with contacts, AUM, allocations, and transactions that are actually verified.
AI is the engine. Dakota Marketplace is the fuel.
Connect the two, in Claude, ChatGPT, or whatever you already use, and the work that used to eat your morning takes minutes, with data you can actually act on.
Book a demo of Dakota Marketplace to get started.
